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Published in final edited form as: Infect Genet Evol. 2011 Dec 3;12(2):274–277. doi: 10.1016/j.meegid.2011.11.004

Functional bias of positively selected genes in Streptococcus genomes

Haruo Suzuki 1, Michael J Stanhope 1,*
PMCID: PMC3294173  NIHMSID: NIHMS351815  PMID: 22155358

Abstract

Rates of nonsynonymous substitution (dN) significantly higher than rates of synonymous substitution (dS) have been used as evidence of positive selection for the fixation of advantageous point mutations. It has been suggested that positive selection contributes to the evolution of virulence factors and certain functional categories in bacterial pathogens. The genus Streptococcus contains a number of important human and agricultural pathogens. Here we assessed positive selection across 13 Streptococcus species, and their relationship with virulence factors and functional categories. We found that known virulence genes were subject to positive selection pressure as much as other genes. After false discovery rate correction for multiple comparisons, no functional categories were significantly over- or underrepresented in positively selected genes relative to other genes. Our results suggest that within the genus Streptococcus positive selection based on dN/dS ratios is not distributed with bias across biological functions.

Keywords: Streptococcus, positive selection, gene functional categories, virulence factor

1. Introduction

Positive selection for the fixation of advantageous point mutations is an important force in the adaptation of microorganisms to different environmental niches, to optimize infection processes and to escape host immune response (Anisimova et al., 2007). There are many reports suggesting that positive selection contributes to the evolution of virulence genes or certain functional categories in bacterial pathogens such as Escherichia coli (Chen et al., 2006), Streptococcus (Anisimova et al., 2007; Lefebure and Stanhope, 2007), Listeria monocytogenes (Orsi et al., 2008), Salmonella (Soyer et al., 2009), and Campylobacter (Lefebure and Stanhope, 2009). A previous analysis of gene families and protein domains from a large and diverse set of species suggested that ‘evidence for positive selection was found principally for gene products with membrane or extracellular localizations, mostly involved in pathogenesis, belonging to different aspects of cell communication or to viral life cycles’ (Aris-Brosou, 2005). It follows that proteins which have important roles in pathogenicity might be more likely to be subject to positive selection.

Streptococcus species cause a wide variety of human diseases, and possesses many different virulence genes (Chen et al., 2005; Davies et al., 2007; Yang et al., 2008). Several studies have reported that positive selection contributes to the evolution of virulence genes in Streptococcus species such as the gene encoding streptokinase (ska) in group A streptococcus (GAS) (Kalia and Bessen, 2004), the virulence gene pauA in Streptococcus uberis (Zadoks et al., 2005), several virulence genes in group B Streptococcus (GBS) (Springman et al., 2009), and the capsule virulence determinant of Streptococcus pneumoniae (Lysenko et al., 2010). It was suggested that positive selection in Streptococcus pyogenes was more frequent in recently transferred genes than in core genes (Marri et al., 2006). A sites test of positive selection found no evidence to support the hypothesis that pathogen-specific accessory genes are more likely to be subject to positive selection than core genes in Streptococcus species (Anisimova et al., 2007) although a large fraction (29%) of positively selected genes can be connected to virulence. A branch-site test of positive selection on core genes of Streptococcus species found positive selection pressure was unevenly distributed across lineages and functional categories, and some of the positively selected genes are known virulence factors (Lefebure and Stanhope, 2007). These previous studies led us to the hypothesis that Streptococcus species have a greater frequency of positive selection in virulence factors and certain functional categories compared to other genes. To test this hypothesis, we assessed positive selection across homologous sequences from the complete genomes of 13 Streptococcus species.

2. Materials and methods

2.1. Data collation

Genome sequence data were manipulated using the Bioperl version 1.6.1 (Stajich et al., 2002) and the G-language Genome Analysis Environment version 1.8.12 (Arakawa et al., 2003). Statistical tests and graphics were implemented using R, version 2.11.1 (R_Development_Core_Team, 2010). Complete genome sequences of 13 Streptococcus strains in GenBank format (Benson et al., 2011) were retrieved from the National Center for Biotechnology Information site at ftp://ftp.ncbi.nih.gov/genomes/Bacteria. One strain was randomly selected from species for which multiple strains have been sequenced (Table 1). All protein-coding sequences longer than 300 nucleotides were retrieved from the 13 Streptococcus strains (Table 1). A group of homologous proteins or protein family was built by all-against-all protein sequence comparison using BLASTP (Altschul et al., 1997) followed by Markov clustering with an inflation factor of 2.5 (van Dongen, 2000). Homologous proteins were identified by BLASTP on the criteria of an E-value cutoff of 1e-5, and minimum aligned sequence length coverage of 50% of a query sequence. This approach yielded 3874 protein families containing 22709 individual proteins from the 13 Streptococcus strains. Alignments required at least four sequences for phylogenetic reconstruction. Of these 3874 protein families, 1485 were retained after excluding (i) protein families shared by less than 4 strains, (ii) those whose ratio of minimum to maximum sequence length were less than 0.5, and (iii) those related to mobile elements such as transposase and integrase.

Table 1.

The 13 Streptococcus strains analyzed in this study.

NCBI Accession Number Organism names
NC_004116 Streptococcus agalactiae 2603V/R
NC_012891 Streptococcus dysgalactiae subsp. equisimilis GGS_124
NC_012471 Streptococcus equi subsp. equi 4047
NC_013798 Streptococcus gallolyticus UCN34
NC_009785 Streptococcus gordonii str. Challis substr. CH1
NC_013853 Streptococcus mitis B6
NC_004350 Streptococcus mutans UA159
NC_003028 Streptococcus pneumoniae TIGR4
NC_002737 Streptococcus pyogenes M1 GAS
NC_009009 Streptococcus sanguinis SK36
NC_009443 Streptococcus suis 98HAH33
NC_006449 Streptococcus thermophilus CNRZ1066
NC_012004 Streptococcus uberis 0140J
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2.2. Positive selection

The homologous sequences were first aligned at the amino acid level using Probalign (Roshan and Livesay, 2006), then backtranslated to nucleotides. Protein families including alignments with >50% of the sites removed, based on a posterior probability cut-off <0.6, were discarded from the analysis. Among the resulting 1264 protein families, the numbers of sequences varied from 4 to 38, and sequence alignment length ranged from 303 to 5310 nucleotides, the median being 876 nucleotides (Supplementary Table S1). The alignments were tested for intragenic recombination based on the single breakpoint (SBP) analysis and KH test in the HyPhy package (Pond et al., 2005; Kosakovsky Pond et al., 2006). A phylogenetic tree was determined for each of the genes using PhyML (Phylogenetic estimation using Maximum Likelihood) (Guindon and Gascuel, 2003; Guindon et al., 2010) with the GTR + Gamma substitution model of nucleotide evolution, and the Subtree Pruning-Regrafting (SPR) branch-swapping method. The median of total branch length of the 1264 gene trees was 4.195 nucleotide substitutions per site (Supplementary Table S1). Each gene tree was used to determine its optimal nucleotide substitution model. To provide evidence of non-neutral evolution, with rates of nonsynonymous substitution (dN) significantly different from rates of synonymous substitution (dS), we used three different codon-based maximum likelihood methods in the HyPhy package (Pond et al., 2005): single-likelihood ancestor counting (SLAC), fixed effects likelihood (FEL), and random effects likelihood (REL). To determine sites evolving non-neutrally, a significance level of 0.05 was used for the SLAC and FEL methods, and a minimum Bayes factor of 95 was used for the REL method. Functional categories from multiple databases were used to assess categories in which positively selected genes were over- or under-represented: the Clusters of Orthologous Groups (COG) (Tatusov et al., 2000; Tatusov et al., 2001), the JCVI role categories (Davidsen et al., 2010), KEGG (Kanehisa and Goto, 2000), SEED (Overbeek et al., 2005), Virulence Factors Database (VFDB) (Chen et al., 2005), and the Gene Ontology (GO) (Ashburner et al., 2000) database. We searched protein sequences against the Pfam (Finn et al., 2010) library of hidden Markov models (HMMs) using HMMER (http://hmmer.janelia.org/), and converted Pfam accession numbers to GO terms using the ‘pfam2go’ mapping (http://www.geneontology.org/external2go/pfam2go). We identified putative virulence factors based on a combination of SEED, VFDB, and MvirDB (Zhou et al., 2007). MvirDB integrates sequence information from multiple microbial databases of protein toxins, virulence factors, and antibiotic resistance genes.

Protein families were classified into two categories: those showing evidence of positive selection (PS+) and those showing no evidence of positive selection (PS•). A 2 × 2 contingency table was constructed for each functional category from the COG, JCVI, KEGG, SEED, VFDB, and GO databases: (a) the number of PS+ in this category; (b) the number of PS+ that are not in this category; (c) the number of PS• in this category; (d) the number of PS• that are not in this category. The odds ratio was calculated as ad/bc. Fisher’s exact test was used to test significance of over- or under-representation of each functional category in PS+ genes relative to PS• genes, and the p-value was adjusted for the multiple comparisons by controlling the false discovery rate (Benjamini and Yekutieli 2001).

3. Results and discussion

We tested for positive selection across homologous genes from the complete genomes of 13 Streptococcus species using three different methods (SLAC, FEL and REL). Of the 1264 protein families tested, 5, 196, and 220 had at least one positively selected site, identified by the SLAC, FEL, and REL methods, respectively (Figure 1). Of these, two protein families, DNA-directed RNA polymerase subunit beta (protein family identification number PFID 315 in Supplementary Table S1) and nucleoside-triphosphatase (PFID 613), were judged as being under positive selection by all the three methods. Two protein families, DNA polymerase III subunit alpha (Gram-positive type) and ADP-ribose pyrophosphatase, were judged as being under positive selection by both the SLAC and FEL methods. A total of 39 protein families were judged as being under positive selection by both the FEL and REL methods. These 39 protein families included a number of putative virulence factors deposited in VFDB (trans-acting positive regulator, atxA, from Bacillus anthracis Sterne) and MvirDB (argininosuccinate lyase, molecular chaperone DnaK, selenocysteine lyase, ascorbate-specific PTS system enzyme IIC from Streptococcus agalactiae, 4-alpha-glucanotransferase from Bacteroides thetaiotaomicron VPI-5482, and carboxylase from Streptomyces coelicolor A3 2). The SLAC, FEL, and REL methods identified 1, 153, and 179 positively selected genes that were unique to each method. A total of 888 protein families showed no evidence of positive selection by any of the three methods. The maximum numbers of positively selected sites for any particular gene detected by the SLAC, FEL, and REL methods were 1, 5, and 30, respectively (Supplementary Table S1). The FEL method identified 5 positively selected sites within the gene encoding alanyl-tRNA synthetase (PFID 489). The REL method found 30 positively selected sites in the feoB gene encoding Fe2+ transport system protein B (PFID 1498), which has been reported as a virulence factor (Iron uptake; Ferrous iron uptake) in other bacteria such as Legionella pneumophila Philadelphia 1 and Staphylococcus aureus subsp. aureus Mu50. REL was relatively liberal (more likely to identify false positive results) in its detection of positively selected sites, SLAC was conservative, and FEL was intermediate, as reported previously (Kosakovsky Pond and Frost, 2005).

Figure 1.

Figure 1

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A venn diagram showing the number of protein families judged as being under positive selection by the three methods (SLAC, FEL, and REL).

There is no tendency for protein families with more sequences to be more likely to show positive selection. The number of sequences showed no effect for SLAC (Wilcoxon rank sum test; p-value = 0.079) and FEL (Wilcoxon rank sum test; p-value = 0.193), although the median number of sequences for positively selected genes (13) was higher than that for other genes (12). The number of sequences showed an opposite effect for REL (Wilcoxon rank sum test; p-value = 0.004), positive selection being more likely to be detected in small data sets. The median number of sequences for positively selected genes (10) was smaller than that for other genes (12).

An earlier study reported that 29% of positively selected genes across species of Streptococcus were known or hypothetical virulence factors in streptococci (Anisimova et al., 2007). Of the 1264 protein families here tested, 406 are known virulence factors of some bacteria, and 103 are known virulence factors of Streptococcus species (Supplementary Table S1). Of the 5 positively selected genes identified by the SLAC method, 2 are virulence factors based on MvirDB: rpoB encoding DNA-directed RNA polymerase subunit beta in Streptococcus agalactiae NEM316 (PFID 315), and the mutT-like protein in Streptomyces coelicolor A3 2 (PFID 674). Of the 196 positively selected genes identified by the FEL method, 61 are virulence factors of some bacteria and 17 are virulence factors of Streptococcus species, including for example Streptococcus pyogenes virulence regulators (SpeB-SpeF extended regulon) (PFID 202), the gene covS encoding two-component sensor histidine kinase in Streptococcus agalactiae 2603V/R (PFID 1158), and M protein trans-acting positive regulator (PFID 1642). Of the 220 positively selected genes identified by the REL method, 71 are virulence factors of some bacteria and 19 are virulence factors of Streptococcus species, including glyceraldehyde 3-phosphate dehydrogenase (PFID 599), and the gene yesM encoding two-component sensor histidine kinase (PFID 1678). Virulence genes were not significantly over- or underrepresented in the set of positively selected genes relative to other genes (Fisher’s exact test p-values were 0.63, 0.80, and 0.87 for SLAC, FEL, and REL, respectively). The results suggest that known virulence genes are subject to positive selection pressure as much as other genes.

We performed enrichment tests to examine over- or underrepresented functional categories in positively selected genes relative to other genes. In positively selected genes identified by the SLAC method, the KEGG pathway map “Purine metabolism” (odds ratio = Infinite) was overrepresented (Supplementary Table S2). In positively selected genes identified by the FEL method, the JCVI subcategory “Protein folding and stabilization” (odds ratio = 8.56) was overrepresented (Supplementary Table S3). In positively selected genes identified by the REL method, the JCVI subcategory “Serine family” (odds ratio = 7.8) was overrepresented, while the JCVI main category “DNA metabolism” (odds ratio = 0.27) was underrepresented (Supplementary Table S4). After false discovery rate correction for multiple comparisons, no functional categories were significantly over- or underrepresented in positively selected genes relative to other genes (P < 0.05).

An earlier study analyzed a database of gene families and protein domains from a large and diverse set of species, and concluded that genes involved in complex functions such as transcription and translation are significantly less likely to undergo positive selection (Aris-Brosou, 2005). In the genus Campylobacter, no GO term was significantly overrepresented in positively selected genes, and ribosome-related genes were never found to be under positive selection (Lefebure and Stanhope, 2009). However, this is not the case in the genus Streptococcus tested in the present study. Of the 37 ribosomal proteins, 0, 6, and 8 were identified as being under positive selection by SLAC, FEL, and REL, respectively. The corresponding odds ratio values for the COG category “Translation, ribosomal structure and biogenesis” were 0, 1.47, and 0.86. Note that SLAC found only five positively selected genes and it is this poor sampling that results in the misleading odds ratio of 0. There are however, potential artifacts involving translational genes related to the confounding effects of codon bias and the detection of positive selection (Aris-Brosou, 2005). It has been shown that highly expressed genes such as ribosomal proteins in fast-growing bacteria exhibit strong codon usage biases, putatively reflecting the action of translational selection to optimize the speed and accuracy of translation (Ikemura, 1985; Eyre-Walker, 1996).

Positive selection can be falsely detected due to the presence of recombination (Anisimova et al., 2003; Shriner et al., 2003). Of the 5, 196, and 220 protein families identified as being under positive selection by the SLAC, FEL, and REL methods, 4, 106, and 106 were also judged as being recombinant based on the single breakpoint (SBP) analysis (Supplementary Table S1). Excluding these recombinant genes from the enrichment tests (data not shown) did not change our main conclusion, i.e., that certain functional categories or virulence factors were not over- or underrepresented in positively selected genes relative to other genes.

4. Conclusions

Our results suggest that known virulence genes from Streptococcus species are subject to positive selection pressure as much as other genes. Previous studies of positive selection in various pathogenic bacteria have revealed many virulence factors that can be under positive selection pressure (Anisimova et al., 2007; Lefebure and Stanhope, 2007; Suzuki et al., 2011). In such cases, researchers inclined to attribute the positive selection to the virulence potential of the locus. However, our findings suggest that virulence factors, at least in Streptococcus, may not be correlated with a history of positive selection. We cannot rule out the possibility that current virulence factor databases such as VFDB and MvirDB (Chen et al., 2005; Zhou et al., 2007; Yang et al., 2008) are far from comprehensive and thus a set of known virulence factors were underestimated. After false discovery rate correction for multiple comparisons, no functional categories were significantly over- or underrepresented in positively selected genes relative to other genes in Streptococcus species. A previous study analyzed a database of gene families and protein domains from a large and diverse set of species, which may mask the uniqueness of particular species groups, and concluded that gene function is strongly correlated with its tendency to undergo adaptive evolution (Aris-Brosou, 2005). Our findings suggest that a correlation of gene function and selection may not be applicable to Streptococcus and by extension possibly other specific genera.

Highlights.

  • We test functional bias of positively selected genes in Streptococcus genomes.

  • Known virulence genes were subject to positive selection as much as other genes.

  • No functional categories were enriched in positively selected genes.

Supplementary Material

01

Acknowledgements

We thank Tristan Lefébure for helpful discussion, Sergei L Kosakovsky Pond for his advice on HyPhy, and Robert Bukowski, Andrew Dolgert and Linda Woodard for their help with the parallelization of the analyses on a Linux cluster at the Computational Biology Service Unit (CBSU) and Center for Advanced Computing (CAC) of Cornell University. This work was supported by the National Institute of Allergy and Infectious Disease, US National Institutes of Health, under grant number AI073368-01A2 awarded to M.J.S.

Footnotes

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